Threat detection of people, vehicles, and other (PVO) as well as person-vehicle interactions (e.g., dismounts) of possible malicious intent are difficult problems due to the complexity of the problem space. The challenges include cluttered scenes with obscured elements (e.g., buildings), varying camera sensor resolutions, different environmental conditions (e.g., illuminations), and unknown motivation of individuals. When there are MUltiple MOving Targets (MUMOTs), there is a need for high-performance computing intelligent machine learning tracking, recognition, threat identification solutions.
Methods and techniques can be incorporated to aid analysts to track and identify dismounts using modern large scale visual sensors such as the Wide-area Motion Imagery (WAMI) systems. Such systems typically produce an overwhelmingly large amount of information. For example, the Autonomous Real-time Ground Ubiquitous Surveillance-Imaging System (ARGUS-IS) produces tens of thousands of moving target indicator (MTI) detections from city-size urban areas (over 40 square kilometers) at video rates of greater than 12 Hz.
The large scale data input challenges existing situational awareness algorithms in time complexity and storage requirements. The lack of computationally efficient MTI analysis tools has become a bottleneck for utilizing WAMI data in urban surveillance. Both hardware and software high-performance computing solutions are sought to handle the large scale data requirements.
Therefore, there is a need to provide a system and method for detecting and tracking multiple moving targets based on wide-area motion imagery to overcome these challenges.